Correcting Misperceptions about Support for Social Distancing to Combat COVID-19

Abstract

Can informing people of high community support for social distancing encourage them to do more of it? We randomly assigned a treatment correcting individuals' underestimates of community support for social distancing. In theory, informing people that more neighbors support social distancing than expected encourages free-riding and lowers the perceived benefits from social distancing. At the same time, the treatment induces people to revise their beliefs about the infectiousness of COVID-19 upwards; this perceived infectiousness effect as well as the norm adherence effect increase the perceived benefits from social distancing. We estimate impacts on social distancing, measured using a combination of self-reports and reports of others. While experts surveyed in advance expected the treatment to increase social distancing, we find that its average effect is close to zero and significantly lower than expert predictions. However, the treatment's effect is heterogeneous, as predicted by theory: it decreases social distancing where current COVID-19 cases are low (where free-riding dominates), but increases it where cases are high (where the perceived-infectiousness effect dominates). These findings highlight that correcting misperceptions may have heterogeneous effects depending on disease prevalence.

Keywords: Behavioral Economics; COVID-19; D91; Health Behavior; Health Policy; I12; O12; Social Distancing.

Figure 1:. The Social Distancing Measure

Notes: Figure shows the breakdown of the social distancing measure at baseline. As pre-specified, respondents considered social distancing (SD) if: 1) self-report “yes” to “In the past 14 days, have you observed the government’s recommendations on social distancing?”, 2) self-report doing more than the sample median number of “social distancing actions” in the past seven days, and 3) are considered to be SD according to leaders and other respondents in the community. Percentages reported are all shares of full sample (N=2,117). See Table 1 and Section 3.3 of the main text for social distancing question definitions.

Figure 2:. District-Level Misperceptions Correction Treatment Effects by COVID-19 Cases

Notes: Misperceptions correction treatment effects (triangles) estimated separately for each of seven districts (with 95% confidence intervals). District-level treatment effects plotted on vertical axis against district-level cumulative COVID-19 case loads at start of endline survey (per 100,000 population) on horizontal axis.